Simultaneous Optimization of both Node and Edge Conservation in Network Alignment via WAVE

  • Yihan Sun
  • Joseph CrawfordEmail author
  • Jie Tang
  • Tijana Milenković
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9289)


Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Existing methods use a node cost function (NCF) to compare nodes across networks and an alignment strategy (AS) to find high-scoring alignments with respect to total NCF over all aligned nodes (or node conservation). Then, they evaluate alignments via a measure that is different than node conservation used to guide alignment construction. Typically, one measures edge conservation, but only after alignments are produced. Hence, we recently directly maximized edge conservation while constructing alignments, which improved their quality. Here, we aim to maximize both node and edge conservation during alignment construction to further improve quality. We design a novel measure of edge conservation that (unlike existing measures that treat each conserved edge the same) weighs conserved edges to favor edges with highly NCF-similar end-nodes. As a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can optimize any measures of node and edge conservation. Using WAVE on top of well-established NCFs improves alignments compared to existing methods that optimize only node or edge conservation or treat each conserved edge the same. We evaluate WAVE on biological data, but it is applicable in any domain.


Gene Ontology Node Pair Marginal Gain Alignment Quality Network Alignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was funded by the National Science Foundation CAREER CCF-1452795 and CCF-1319469 grants.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Yihan Sun
    • 1
    • 2
    • 3
  • Joseph Crawford
    • 1
    Email author
  • Jie Tang
    • 2
  • Tijana Milenković
    • 1
  1. 1.Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global HealthUniversity of Notre DameNotre DameUSA
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.Computer Science DepartmentCarnegie Mellon UniversityPittsburghPennsylvania

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